Infectious disease epidemics are spatially dependent processes relying on patterns of host-pathogen contact, vector movement, host-pathogen dispersal, and the spatial structure of the genetic and physical environment. These factors act in concert to influence the direction, intensity and velocity of epidemics. Spatial analysis of infectious disease has primarily focused either on statistical assessments of the geographic distribution of case occurrence or on the development of dynamic models of spread. Rarely, are two the approaches combined, usually because there are insufficiently detailed spatial-temporal data that can be used as the basis for modeling. However, the investigators on this proposal plan to combine these two aspects of spatial analysis. The CDC has complied an extensive spatial-temporal data set for rabies emergence in the Mid-Atlantic United States. Most of the rabies epizootic is associated with spread in raccoons (> 40,000 cases). The size and detail of this data set allows for an unprecedented attempt to link statistical pattern analysis with predictive dynamic modeling. The investigators will focus on the epidemic that occurred in Connecticut from 1991-96 as a basis. They intend to: 1) derive the local wave-front velocities of disease spread using vector field analysis of the spatial-temporal data from the Connecticut epidemic of raccoon rabies, 2) associate local wave-front velocities in the Connecticut epidemic with key GIS environmental variables, and 3) construct predictive cellular automata models of disease spread based on the statistical analysis of the Connecticut epidemic and the GIS variables. These models will be parameterized using the vector-field/GIS patterns of statistical association, and then tested and applied to ongoing and newly emerging rabies epidemics.